Maximum Entropy Based Associative Regression for Sparse Datasets

We propose a supervised learning technique defining significant frequent patterns for associative regression. Assuming frequent patterns quantify correlations in dataset, we constrain the Generalized Iterative Scaling (GIS) convergence algorithm for Maximum Entropy (ME) models. We have used the combinations of ME parameters and GIS probabilities as discriminative weights to frequent patterns. The weighted frequent patterns then order the predictive analytics output. Experiments are conducted on sparse numeric datasets. Results suggest that condensed representations of frequent patterns allow parametric models suitable for class association rule mining.